Read duplicates
all_data %>%
select(dataset,Extraction,duplicates,Taxon) %>%
unique() %>%
group_by(Taxon,Extraction) %>%
summarise(value = sprintf("%.1f±%.1f", mean(duplicates), sd(duplicates))) %>%
pivot_wider(names_from = Extraction, values_from = value) %>%
tt(caption = "Mean and standard deviation of fraction of duplicated reads")
tinytable_7c0az2p7y96m2rjip6xl
Mean and standard deviation of fraction of duplicated reads
| Taxon |
DREX |
EHEX |
ZYMO |
| Amphibian |
0.2±0.2 |
0.2±0.2 |
0.3±0.2 |
| Bird |
0.9±0.1 |
0.6±0.4 |
0.8±0.3 |
| Control |
0.9±0.0 |
1.0±0.0 |
1.0±0.0 |
| Mammal |
0.2±0.1 |
0.2±0.2 |
0.4±0.4 |
| Reptile |
0.3±0.3 |
0.4±0.4 |
0.5±0.4 |
all_data %>%
select(dataset,Extraction,duplicates,Taxon) %>%
unique() %>%
mutate(Taxon=factor(Taxon,levels=c("Amphibian","Reptile","Mammal","Bird","Control"))) %>%
mutate(Extraction=factor(Extraction,levels=c("ZYMO","DREX","EHEX"))) %>%
ggplot(aes(x=Extraction,y=duplicates))+
geom_boxplot() +
facet_grid(. ~ Taxon, scales = "free") +
labs(y="Duplication rate",x="Extraction method")

all_data %>%
select(dataset,Sample,Species,Extraction,duplicates,Taxon) %>%
filter(Taxon != "Control") %>%
lmerTest::lmer(duplicates ~ Extraction + (1 | Sample) + (1 | Species), data = ., REML = FALSE) %>%
broom.mixed::tidy() %>%
tt()
tinytable_171zculjd8c8psm2h61h
| effect |
group |
term |
estimate |
std.error |
statistic |
df |
p.value |
| fixed |
NA |
(Intercept) |
0.39361067 |
0.07257915 |
5.423192 |
14.33359 |
8.267782e-05 |
| fixed |
NA |
ExtractionEHEX |
-0.05339005 |
0.03918499 |
-1.362513 |
160.73104 |
1.749423e-01 |
| fixed |
NA |
ExtractionZYMO |
0.09279626 |
0.03900562 |
2.379048 |
160.72377 |
1.853073e-02 |
| ran_pars |
Sample |
sd__(Intercept) |
0.00000000 |
NA |
NA |
NA |
NA |
| ran_pars |
Species |
sd__(Intercept) |
0.23148121 |
NA |
NA |
NA |
NA |
| ran_pars |
Residual |
sd__Observation |
0.21005171 |
NA |
NA |
NA |
NA |
Depth of coverage
all_data %>%
select(dataset,Extraction,coverage_depth,Taxon) %>%
unique() %>%
group_by(Taxon,Extraction) %>%
summarise(value = sprintf("%.1f±%.1f", mean(coverage_depth), sd(coverage_depth))) %>%
pivot_wider(names_from = Extraction, values_from = value) %>%
tt(caption = "Mean and standard deviation of fraction of duplicated reads")
tinytable_cj4ehdohrsqvx8h9nn8x
Mean and standard deviation of fraction of duplicated reads
| Taxon |
DREX |
EHEX |
ZYMO |
| Amphibian |
0.0±0.0 |
0.0±0.0 |
0.0±0.0 |
| Bird |
0.6±0.5 |
0.8±0.8 |
0.4±0.8 |
| Control |
0.0±0.0 |
0.0±0.0 |
0.0±0.0 |
| Mammal |
0.3±0.4 |
0.5±1.0 |
0.7±1.2 |
| Reptile |
0.1±0.1 |
0.1±0.1 |
0.2±0.3 |
all_data %>%
select(dataset,Extraction,coverage_depth,Taxon) %>%
unique() %>%
mutate(Taxon=factor(Taxon,levels=c("Amphibian","Reptile","Mammal","Bird","Control"))) %>%
mutate(Extraction=factor(Extraction,levels=c("ZYMO","DREX","EHEX"))) %>%
ggplot(aes(x=Extraction,y=coverage_depth))+
geom_boxplot() +
facet_grid(. ~ Taxon, scales = "free") +
labs(y="Depth of coverage",x="Extraction method")

all_data %>%
select(dataset,Sample,Species,Extraction,coverage_depth,Taxon) %>%
unique() %>%
filter(Taxon != "Control") %>%
lmerTest::lmer(coverage_depth ~ Extraction + (1 | Sample) + (1 | Species), data = ., REML = FALSE) %>%
broom.mixed::tidy() %>%
tt()
tinytable_uezua2r41tz836tqbm7y
| effect |
group |
term |
estimate |
std.error |
statistic |
df |
p.value |
| fixed |
NA |
(Intercept) |
0.23933333 |
0.1331063 |
1.7980622 |
20.58494 |
0.0868510 |
| fixed |
NA |
ExtractionEHEX |
0.08700000 |
0.1144640 |
0.7600640 |
48.00000 |
0.4509335 |
| fixed |
NA |
ExtractionZYMO |
0.08979167 |
0.1144640 |
0.7844531 |
48.00000 |
0.4366287 |
| ran_pars |
Sample |
sd__(Intercept) |
0.40863458 |
NA |
NA |
NA |
NA |
| ran_pars |
Species |
sd__(Intercept) |
0.22473121 |
NA |
NA |
NA |
NA |
| ran_pars |
Residual |
sd__Observation |
0.39651507 |
NA |
NA |
NA |
NA |
Breadth of coverage
all_data %>%
select(dataset,Extraction,coverage_breadth,Taxon) %>%
unique() %>%
group_by(Taxon,Extraction) %>%
summarise(value = sprintf("%.1f±%.1f", mean(coverage_breadth), sd(coverage_breadth))) %>%
pivot_wider(names_from = Extraction, values_from = value) %>%
tt(caption = "Mean and standard deviation of depth of host genome coverage")
tinytable_h4kmi71tafw9a17ypn4b
Mean and standard deviation of depth of host genome coverage
| Taxon |
DREX |
EHEX |
ZYMO |
| Amphibian |
0.0±0.0 |
0.0±0.0 |
0.0±0.0 |
| Bird |
3.2±4.4 |
8.9±13.9 |
0.6±0.5 |
| Control |
0.0±0.0 |
0.0±0.0 |
0.0±0.0 |
| Mammal |
10.2±16.4 |
15.1±26.4 |
5.7±5.9 |
| Reptile |
3.0±5.4 |
2.9±3.7 |
4.9±7.5 |
all_data %>%
select(dataset,Extraction,coverage_breadth,Taxon) %>%
unique() %>%
mutate(Taxon=factor(Taxon,levels=c("Amphibian","Reptile","Mammal","Bird","Control"))) %>%
mutate(Extraction=factor(Extraction,levels=c("ZYMO","DREX","EHEX"))) %>%
ggplot(aes(x=Extraction,y=coverage_breadth))+
geom_boxplot() +
facet_grid(. ~ Taxon, scales = "free") +
labs(y="Breadth of coverage",x="Extraction method")

all_data %>%
select(dataset,Extraction,Sample,Species,coverage_breadth,Taxon) %>%
unique() %>%
filter(Taxon != "Control") %>%
lmerTest::lmer(coverage_breadth ~ Extraction + (1 | Sample) + (1 | Species), data = ., REML = FALSE) %>%
broom.mixed::tidy() %>%
tt()
tinytable_aqgng9yy5dvdrgm75m4y
| effect |
group |
term |
estimate |
std.error |
statistic |
df |
p.value |
| fixed |
NA |
(Intercept) |
4.100417 |
2.252826 |
1.8201211 |
20.82558 |
0.08314479 |
| fixed |
NA |
ExtractionEHEX |
2.617500 |
1.956426 |
1.3378986 |
48.00000 |
0.18723379 |
| fixed |
NA |
ExtractionZYMO |
-1.301250 |
1.956426 |
-0.6651158 |
48.00000 |
0.50915975 |
| ran_pars |
Sample |
sd__(Intercept) |
7.118462 |
NA |
NA |
NA |
NA |
| ran_pars |
Species |
sd__(Intercept) |
3.549767 |
NA |
NA |
NA |
NA |
| ran_pars |
Residual |
sd__Observation |
6.777259 |
NA |
NA |
NA |
NA |